Today, AI is used more as a buzzword in conversations, however in reality it depends on the company when, where and how they use AI and ML. What matters is what stage is the company at, that is what determines if the use of AI and ML is apt or not. For example, if the company is a startup, then the said data can be managed with the use of excel sheets and if the company is mid-sized then relevant engines can manage the data. One does not necessarily need AI and ML models to manage this level of data. If a company even then goes to apply AI/ML there is a high possibility that it might not yield the right results as there isn’t enough data to train these models.
Even at Amazon, I didn’t use AI/ML on the first day, for the system I first scaled the business to billions of dollars only then did we begin to learn and implement the system. We took regular feedback on the engine and using that trained the data to know what to deposit, how to classify the product, etc… all of this can only be done after a point in the business. With the cost of competition and storage coming down along with companies like Amazon, AWS, Google, Microsoft, etc. that provide AI and ML features; the overall cost of AI/ML has reduced across the board. But I’ve seen that when it comes to third parties, i.e, especially startup companies, they come up with solutions where AI/ML is used in such a way that they aggregate the data, normalize the data and test the model. As a single company, you may not have the desired data but as a group of companies you will have the collective data and you can train the model without compromising on the security of the data. Thus you will be able to provide solutions to other companies.
In many areas such as consumer behavior and B2B companies among others, AI is already adopted, it is not a gimmick any longer, it has long become mainstream. Whether you know it or not, you and I use AI/ML every day either in the form of talking to Alexa, watching something on Netflix, or the form of online shopping; you name it and it’s all AI and ML. AI and ML are working behind the scene but no one is labeling it as such. Thus it has become mainstream.
At Upstox we have scaled, we started with spreadsheets, whereas now we are at an 8 million customer base company, which makes it difficult for us to just add manual resources. After a point it is not a viable solution, what we did is create a data strategy, we started collecting data from different sources and collating it in one place. All business units have access to and can use this collated data. Now from that data, we get customer insights, segment insights, trends and where and all can AI and ML be applied. We have already begun to apply AI/ML in service areas, where we can serve our customers better. We are looking at the recommendations, that is how we recommend the right content to the right consumer. We are also looking at some personalization experience, we are doing various experiments in this space as well, definitely, AI and ML will be a part of Upstox and continue to build the system and provide scalability.